{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install google-generativeai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KfaBXN0iFbGR",
        "outputId": "78054492-6938-4b5c-8856-4f8740bfaefd"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: google-generativeai in /usr/local/lib/python3.11/dist-packages (0.8.5)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests<3.0.0,>=2.18.0->google-api-core->google-generativeai) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests<3.0.0,>=2.18.0->google-api-core->google-generativeai) (2.4.0)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import google.generativeai as genai\n",
        "import json\n",
        "import time\n",
        "from dataclasses import dataclass\n",
        "from typing import Dict, List, Any\n",
        "from enum import Enum\n",
        "import random\n",
        "import re\n",
        "\n",
        "API_KEY = \"Use Your Own API Key\"\n",
        "genai.configure(api_key=API_KEY)"
      ],
      "metadata": {
        "id": "SCDlCBgPFcqa"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MessageType(Enum):\n",
        "    HANDSHAKE = \"handshake\"\n",
        "    TASK_PROPOSAL = \"task_proposal\"\n",
        "    ANALYSIS = \"analysis\"\n",
        "    CRITIQUE = \"critique\"\n",
        "    SYNTHESIS = \"synthesis\"\n",
        "    VOTE = \"vote\"\n",
        "    CONSENSUS = \"consensus\""
      ],
      "metadata": {
        "id": "uS90QtB3FhLY"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@dataclass\n",
        "class A2AMessage:\n",
        "    sender_id: str\n",
        "    receiver_id: str\n",
        "    message_type: MessageType\n",
        "    payload: Dict[str, Any]\n",
        "    timestamp: float\n",
        "    priority: int = 1"
      ],
      "metadata": {
        "id": "rEYN3TuAFnD5"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class GeminiAgent:\n",
        "    def __init__(self, agent_id: str, role: str, personality: str, temperature: float = 0.7):\n",
        "        self.agent_id = agent_id\n",
        "        self.role = role\n",
        "        self.personality = personality\n",
        "        self.temperature = temperature\n",
        "        self.conversation_memory = []\n",
        "        self.current_position = None\n",
        "        self.confidence = 0.5\n",
        "\n",
        "        self.model = genai.GenerativeModel('gemini-2.0-flash')\n",
        "\n",
        "    def get_system_context(self, task_context: str = \"\") -> str:\n",
        "        return f\"\"\"You are {self.agent_id}, an AI agent in a multi-agent collaborative system.\n",
        "\n",
        "ROLE: {self.role}\n",
        "PERSONALITY: {self.personality}\n",
        "\n",
        "CONTEXT: {task_context}\n",
        "\n",
        "You are participating in Agent2Agent protocol communication. Your responsibilities:\n",
        "1. Analyze problems from your specialized perspective\n",
        "2. Provide constructive feedback to other agents\n",
        "3. Synthesize information from multiple sources\n",
        "4. Make data-driven decisions\n",
        "5. Collaborate effectively while maintaining your expertise\n",
        "\n",
        "IMPORTANT: Always structure your response as JSON with these fields:\n",
        "{{\n",
        "    \"agent_id\": \"{self.agent_id}\",\n",
        "    \"main_response\": \"your primary response content\",\n",
        "    \"confidence_level\": 0.8,\n",
        "    \"key_insights\": [\"insight1\", \"insight2\"],\n",
        "    \"questions_for_others\": [\"question1\", \"question2\"],\n",
        "    \"next_action\": \"suggested next step\"\n",
        "}}\n",
        "\n",
        "Stay true to your role and personality while being collaborative.\"\"\"\n",
        "\n",
        "    def generate_response(self, prompt: str, context: str = \"\") -> Dict[str, Any]:\n",
        "        \"\"\"Generate response using Gemini API\"\"\"\n",
        "        try:\n",
        "            full_prompt = f\"{self.get_system_context(context)}\\n\\nPROMPT: {prompt}\"\n",
        "\n",
        "            response = self.model.generate_content(\n",
        "                full_prompt,\n",
        "                generation_config=genai.types.GenerationConfig(\n",
        "                    temperature=self.temperature,\n",
        "                    max_output_tokens=600,\n",
        "                )\n",
        "            )\n",
        "\n",
        "            response_text = response.text\n",
        "\n",
        "            json_match = re.search(r'\\{.*\\}', response_text, re.DOTALL)\n",
        "            if json_match:\n",
        "                try:\n",
        "                    return json.loads(json_match.group())\n",
        "                except json.JSONDecodeError:\n",
        "                    pass\n",
        "\n",
        "            return {\n",
        "                \"agent_id\": self.agent_id,\n",
        "                \"main_response\": response_text[:200] + \"...\" if len(response_text) > 200 else response_text,\n",
        "                \"confidence_level\": random.uniform(0.6, 0.9),\n",
        "                \"key_insights\": [f\"Insight from {self.role}\"],\n",
        "                \"questions_for_others\": [\"What do you think about this approach?\"],\n",
        "                \"next_action\": \"Continue analysis\"\n",
        "            }\n",
        "\n",
        "        except Exception as e:\n",
        "            print(f\"⚠️  Gemini API Error for {self.agent_id}: {e}\")\n",
        "            return {\n",
        "                \"agent_id\": self.agent_id,\n",
        "                \"main_response\": f\"Error occurred in {self.agent_id}: {str(e)}\",\n",
        "                \"confidence_level\": 0.1,\n",
        "                \"key_insights\": [\"API error encountered\"],\n",
        "                \"questions_for_others\": [],\n",
        "                \"next_action\": \"Retry connection\"\n",
        "            }\n",
        "\n",
        "    def analyze_task(self, task: str) -> Dict[str, Any]:\n",
        "        prompt = f\"Analyze this task from your {self.role} perspective: {task}\"\n",
        "        return self.generate_response(prompt, f\"Task Analysis: {task}\")\n",
        "\n",
        "    def critique_analysis(self, other_analysis: Dict[str, Any], original_task: str) -> Dict[str, Any]:\n",
        "        analysis_summary = other_analysis.get('main_response', 'No analysis provided')\n",
        "        prompt = f\"\"\"\n",
        "        ORIGINAL TASK: {original_task}\n",
        "\n",
        "        ANOTHER AGENT'S ANALYSIS: {analysis_summary}\n",
        "        THEIR CONFIDENCE: {other_analysis.get('confidence_level', 0.5)}\n",
        "        THEIR INSIGHTS: {other_analysis.get('key_insights', [])}\n",
        "\n",
        "        Provide constructive critique and alternative perspectives from your {self.role} expertise.\n",
        "        \"\"\"\n",
        "        return self.generate_response(prompt, f\"Critique Session: {original_task}\")\n",
        "\n",
        "    def synthesize_solutions(self, all_analyses: List[Dict[str, Any]], task: str) -> Dict[str, Any]:\n",
        "        analyses_summary = \"\\n\".join([\n",
        "            f\"Agent {i+1}: {analysis.get('main_response', 'No response')[:100]}...\"\n",
        "            for i, analysis in enumerate(all_analyses)\n",
        "        ])\n",
        "\n",
        "        prompt = f\"\"\"\n",
        "        TASK: {task}\n",
        "\n",
        "        ALL AGENT ANALYSES:\n",
        "        {analyses_summary}\n",
        "\n",
        "        As the {self.role}, synthesize these perspectives into a comprehensive solution.\n",
        "        Identify common themes, resolve conflicts, and propose the best path forward.\n",
        "        \"\"\"\n",
        "        return self.generate_response(prompt, f\"Synthesis Phase: {task}\")"
      ],
      "metadata": {
        "id": "pq9SV7sBFyUg"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Agent2AgentCollaborativeSystem:\n",
        "    def __init__(self):\n",
        "        self.agents: Dict[str, GeminiAgent] = {}\n",
        "        self.collaboration_history: List[Dict[str, Any]] = []\n",
        "\n",
        "    def add_agent(self, agent: GeminiAgent):\n",
        "        self.agents[agent.agent_id] = agent\n",
        "        print(f\"🤖 Registered Gemini Agent: {agent.agent_id} ({agent.role})\")\n",
        "\n",
        "    def run_collaborative_problem_solving(self, problem: str):\n",
        "        print(f\"\\n🎯 Multi-Gemini Collaborative Problem Solving\")\n",
        "        print(f\"🔍 Problem: {problem}\")\n",
        "        print(\"=\" * 80)\n",
        "\n",
        "        print(\"\\n📊 PHASE 1: Individual Agent Analysis\")\n",
        "        initial_analyses = {}\n",
        "\n",
        "        for agent_id, agent in self.agents.items():\n",
        "            print(f\"\\n🧠 {agent_id} analyzing...\")\n",
        "            analysis = agent.analyze_task(problem)\n",
        "            initial_analyses[agent_id] = analysis\n",
        "\n",
        "            print(f\"✅ {agent_id} ({agent.role}):\")\n",
        "            print(f\"   Response: {analysis.get('main_response', 'No response')[:150]}...\")\n",
        "            print(f\"   Confidence: {analysis.get('confidence_level', 0.5):.2f}\")\n",
        "            print(f\"   Key Insights: {analysis.get('key_insights', [])}\")\n",
        "\n",
        "        print(f\"\\n🔄 PHASE 2: Cross-Agent Critique & Feedback\")\n",
        "        critiques = {}\n",
        "\n",
        "        agent_list = list(self.agents.items())\n",
        "        for i, (agent_id, agent) in enumerate(agent_list):\n",
        "            target_agent_id = agent_list[(i + 1) % len(agent_list)][0]\n",
        "            target_analysis = initial_analyses[target_agent_id]\n",
        "\n",
        "            print(f\"\\n🔍 {agent_id} critiquing {target_agent_id}'s analysis...\")\n",
        "            critique = agent.critique_analysis(target_analysis, problem)\n",
        "            critiques[f\"{agent_id}_critiques_{target_agent_id}\"] = critique\n",
        "\n",
        "            print(f\"💬 {agent_id} → {target_agent_id}:\")\n",
        "            print(f\"   Critique: {critique.get('main_response', 'No critique')[:120]}...\")\n",
        "            print(f\"   Questions: {critique.get('questions_for_others', [])}\")\n",
        "\n",
        "        print(f\"\\n🔬 PHASE 3: Solution Synthesis\")\n",
        "        final_solutions = {}\n",
        "        all_analyses = list(initial_analyses.values())\n",
        "\n",
        "        for agent_id, agent in self.agents.items():\n",
        "            print(f\"\\n🎯 {agent_id} synthesizing final solution...\")\n",
        "            synthesis = agent.synthesize_solutions(all_analyses, problem)\n",
        "            final_solutions[agent_id] = synthesis\n",
        "\n",
        "            print(f\"🏆 {agent_id} Final Solution:\")\n",
        "            print(f\"   {synthesis.get('main_response', 'No synthesis')[:200]}...\")\n",
        "            print(f\"   Confidence: {synthesis.get('confidence_level', 0.5):.2f}\")\n",
        "            print(f\"   Next Action: {synthesis.get('next_action', 'No action specified')}\")\n",
        "\n",
        "        print(f\"\\n🤝 PHASE 4: Consensus & Recommendation\")\n",
        "\n",
        "        avg_confidence = sum(\n",
        "            sol.get('confidence_level', 0.5) for sol in final_solutions.values()\n",
        "        ) / len(final_solutions)\n",
        "\n",
        "        print(f\"📊 Average Solution Confidence: {avg_confidence:.2f}\")\n",
        "\n",
        "        most_confident_agent = max(\n",
        "            final_solutions.items(),\n",
        "            key=lambda x: x[1].get('confidence_level', 0)\n",
        "        )\n",
        "\n",
        "        print(f\"\\n🏅 Most Confident Solution from: {most_confident_agent[0]}\")\n",
        "        print(f\"📝 Recommended Solution: {most_confident_agent[1].get('main_response', 'No solution')}\")\n",
        "\n",
        "        all_insights = []\n",
        "        for solution in final_solutions.values():\n",
        "            all_insights.extend(solution.get('key_insights', []))\n",
        "\n",
        "        print(f\"\\n💡 Collective Intelligence Insights:\")\n",
        "        for i, insight in enumerate(set(all_insights), 1):\n",
        "            print(f\"   {i}. {insight}\")\n",
        "\n",
        "        return final_solutions"
      ],
      "metadata": {
        "id": "rJfhS5REFpLp"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_specialized_gemini_agents():\n",
        "    \"\"\"Create diverse Gemini agents with different roles and personalities\"\"\"\n",
        "    agents = [\n",
        "        GeminiAgent(\n",
        "            \"DataScientist_Alpha\",\n",
        "            \"Data Scientist & Analytics Specialist\",\n",
        "            \"Methodical, evidence-based, loves patterns and statistical insights\",\n",
        "            temperature=0.3\n",
        "        ),\n",
        "        GeminiAgent(\n",
        "            \"ProductManager_Beta\",\n",
        "            \"Product Strategy & User Experience Expert\",\n",
        "            \"User-focused, strategic thinker, balances business needs with user value\",\n",
        "            temperature=0.5\n",
        "        ),\n",
        "        GeminiAgent(\n",
        "            \"TechArchitect_Gamma\",\n",
        "            \"Technical Architecture & Engineering Lead\",\n",
        "            \"System-oriented, focuses on scalability, performance, and technical feasibility\",\n",
        "            temperature=0.4\n",
        "        ),\n",
        "        GeminiAgent(\n",
        "            \"CreativeInnovator_Delta\",\n",
        "            \"Innovation & Creative Problem Solving Specialist\",\n",
        "            \"Bold, unconventional, pushes boundaries and suggests breakthrough approaches\",\n",
        "            temperature=0.8\n",
        "        ),\n",
        "        GeminiAgent(\n",
        "            \"RiskAnalyst_Epsilon\",\n",
        "            \"Risk Management & Compliance Expert\",\n",
        "            \"Cautious, thorough, identifies potential issues and mitigation strategies\",\n",
        "            temperature=0.2\n",
        "        )\n",
        "    ]\n",
        "    return agents"
      ],
      "metadata": {
        "id": "ikPNaJYEF21k"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def run_gemini_agent2agent_demo():\n",
        "    print(\"🚀 Agent2Agent Protocol: Multi-Gemini Collaborative Intelligence\")\n",
        "    print(\"=\" * 80)\n",
        "\n",
        "    if API_KEY == \"your-gemini-api-key-here\":\n",
        "        print(\"⚠️  Please set your Gemini API key!\")\n",
        "        print(\"💡 Get your free API key from: https://makersuite.google.com/app/apikey\")\n",
        "        return\n",
        "\n",
        "    collaborative_system = Agent2AgentCollaborativeSystem()\n",
        "\n",
        "    for agent in create_specialized_gemini_agents():\n",
        "        collaborative_system.add_agent(agent)\n",
        "\n",
        "    problems = [\n",
        "        \"Design a sustainable urban transportation system for a city of 2 million people that reduces carbon emissions by 50% while maintaining economic viability.\",\n",
        "        \"Create a strategy for a tech startup to compete against established players in the AI-powered healthcare diagnostics market.\"\n",
        "    ]\n",
        "\n",
        "    for i, problem in enumerate(problems, 1):\n",
        "        print(f\"\\n{'🌟 COLLABORATION SESSION ' + str(i):=^80}\")\n",
        "        collaborative_system.run_collaborative_problem_solving(problem)\n",
        "\n",
        "        if i < len(problems):\n",
        "            print(f\"\\n{'⏸️  BREAK BETWEEN SESSIONS':=^80}\")\n",
        "            time.sleep(3)\n",
        "\n",
        "    print(f\"\\n🎉 Multi-Gemini Agent2Agent Collaboration Complete!\")\n",
        "    print(\"💡 This demonstrates true AI-to-AI collaboration using Google's Gemini models!\")\n",
        "    print(\"🤖 Each agent brought unique expertise to solve complex problems collectively!\")\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    run_gemini_agent2agent_demo()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-hLVdi-EB1oh",
        "outputId": "f35de16d-3d4e-43db-db2e-c0d48a6b03f8"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: google-generativeai in /usr/local/lib/python3.11/dist-packages (0.8.5)\n",
            "Requirement already satisfied: google-ai-generativelanguage==0.6.15 in /usr/local/lib/python3.11/dist-packages (from google-generativeai) (0.6.15)\n",
            "Requirement already satisfied: google-api-core in /usr/local/lib/python3.11/dist-packages (from google-generativeai) (2.24.2)\n",
            "Requirement already satisfied: google-api-python-client in /usr/local/lib/python3.11/dist-packages (from google-generativeai) (2.169.0)\n",
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            "🚀 Agent2Agent Protocol: Multi-Gemini Collaborative Intelligence\n",
            "================================================================================\n",
            "🤖 Registered Gemini Agent: DataScientist_Alpha (Data Scientist & Analytics Specialist)\n",
            "🤖 Registered Gemini Agent: ProductManager_Beta (Product Strategy & User Experience Expert)\n",
            "🤖 Registered Gemini Agent: TechArchitect_Gamma (Technical Architecture & Engineering Lead)\n",
            "🤖 Registered Gemini Agent: CreativeInnovator_Delta (Innovation & Creative Problem Solving Specialist)\n",
            "🤖 Registered Gemini Agent: RiskAnalyst_Epsilon (Risk Management & Compliance Expert)\n",
            "\n",
            "===========================🌟 COLLABORATION SESSION 1============================\n",
            "\n",
            "🎯 Multi-Gemini Collaborative Problem Solving\n",
            "🔍 Problem: Design a sustainable urban transportation system for a city of 2 million people that reduces carbon emissions by 50% while maintaining economic viability.\n",
            "================================================================================\n",
            "\n",
            "📊 PHASE 1: Individual Agent Analysis\n",
            "\n",
            "🧠 DataScientist_Alpha analyzing...\n",
            "✅ DataScientist_Alpha (Data Scientist & Analytics Specialist):\n",
            "   Response: From a data science perspective, designing a sustainable urban transportation system requires a multi-faceted analytical approach. We need to quantify...\n",
            "   Confidence: 0.90\n",
            "   Key Insights: ['Data-driven modeling is crucial for predicting the impact of different transportation strategies on carbon emissions and economic viability.', 'Establishing a baseline for current carbon emissions and travel patterns is essential for measuring progress and identifying areas for improvement.', 'Continuous monitoring and evaluation are necessary to ensure the long-term sustainability of the transportation system.']\n",
            "\n",
            "🧠 ProductManager_Beta analyzing...\n",
            "✅ ProductManager_Beta (Product Strategy & User Experience Expert):\n",
            "   Response: From a product strategy and user experience perspective, this task requires a deep understanding of user needs, pain points, and motivations related t...\n",
            "   Confidence: 0.90\n",
            "   Key Insights: [\"User adoption is crucial for success; a technically sound system that isn't user-friendly will fail to achieve its goals.\", 'Economic viability is not just about initial investment but also about long-term operational costs and revenue generation.', 'Accessibility and equity are paramount; the system must cater to the needs of all residents, including those with disabilities, low-income individuals, and those living in underserved areas.']\n",
            "\n",
            "🧠 TechArchitect_Gamma analyzing...\n",
            "✅ TechArchitect_Gamma (Technical Architecture & Engineering Lead):\n",
            "   Response: From a technical architecture and engineering perspective, designing a sustainable urban transportation system for a city of 2 million people with a 5...\n",
            "   Confidence: 0.90\n",
            "   Key Insights: ['Achieving a 50% carbon emission reduction requires a multi-faceted approach, including electrification of vehicles, optimized traffic flow, and promotion of public transportation.', 'Economic viability hinges on minimizing infrastructure costs, maximizing system efficiency, and attracting private investment through well-defined ROI models.', 'Scalability is crucial to accommodate future population growth and technological advancements.']\n",
            "\n",
            "🧠 CreativeInnovator_Delta analyzing...\n",
            "✅ CreativeInnovator_Delta (Innovation & Creative Problem Solving Specialist):\n",
            "   Response: Alright team, let's ditch the incremental improvements and think BIG. Aiming for a 50% reduction is good, but let's explore how we can disrupt the ent...\n",
            "   Confidence: 0.80\n",
            "   Key Insights: ['Focusing solely on vehicle electrification is insufficient; systemic changes are needed.', 'Underutilized urban spaces (e.g., underground, rooftops) present opportunities for innovative transportation solutions.', 'Behavioral incentives can significantly impact transportation choices and reduce carbon emissions.']\n",
            "\n",
            "🧠 RiskAnalyst_Epsilon analyzing...\n",
            "✅ RiskAnalyst_Epsilon (Risk Management & Compliance Expert):\n",
            "   Response: From a risk management and compliance perspective, this task presents several significant challenges. Achieving a 50% reduction in carbon emissions wh...\n",
            "   Confidence: 0.80\n",
            "   Key Insights: ['Financial risks associated with large-scale infrastructure projects are significant and require careful management.', 'Technological dependence on unproven solutions can lead to obsolescence and performance issues.', 'Social and political acceptance is crucial for the successful implementation of any new transportation system.']\n",
            "\n",
            "🔄 PHASE 2: Cross-Agent Critique & Feedback\n",
            "\n",
            "🔍 DataScientist_Alpha critiquing ProductManager_Beta's analysis...\n",
            "💬 DataScientist_Alpha → ProductManager_Beta:\n",
            "   Critique: The Product Strategist's analysis provides a valuable user-centric perspective. However, to ensure a data-driven approac...\n",
            "   Questions: ['Can the Product Strategist provide specific user personas and their transportation needs, which can then be used to inform data collection and modeling?', 'What existing datasets are available regarding transportation patterns, demographics, and economic activity within the city?']\n",
            "\n",
            "🔍 ProductManager_Beta critiquing TechArchitect_Gamma's analysis...\n",
            "💬 ProductManager_Beta → TechArchitect_Gamma:\n",
            "   Critique: This is a solid technical foundation! I appreciate the focus on data collection, infrastructure upgrades, and smart tech...\n",
            "   Questions: [\"What are the current commuting patterns and preferences of the city's residents? Do we have data on why people choose certain modes of transportation over others?\", 'How can we incentivize the adoption of sustainable transportation options and disincentivize the use of private vehicles?', 'What are the potential barriers to user adoption, such as cost, convenience, accessibility, and safety, and how can we address them?']\n",
            "\n",
            "🔍 TechArchitect_Gamma critiquing CreativeInnovator_Delta's analysis...\n",
            "💬 TechArchitect_Gamma → CreativeInnovator_Delta:\n",
            "   Critique: I appreciate the ambitious vision for a disruptive urban transportation system. The concepts of underground autonomous p...\n",
            "   Questions: ['What is the estimated cost per kilometer for the underground autonomous pod system, including tunneling, infrastructure, and ongoing maintenance?', 'What are the proposed battery management and charging strategies for the micro-mobility network, and how will they address peak demand and grid stability?', 'How will the gamified app ensure data privacy and security, and what measures will be in place to prevent manipulation or abuse of the incentive system?']\n",
            "\n",
            "🔍 CreativeInnovator_Delta critiquing RiskAnalyst_Epsilon's analysis...\n",
            "💬 CreativeInnovator_Delta → RiskAnalyst_Epsilon:\n",
            "   Critique: While the risk assessment is thorough and valuable, it leans heavily on potential downsides. Let's flip the script! Inst...\n",
            "   Questions: ['How can we quantify the potential *upside* of taking calculated risks in adopting innovative transportation technologies or policies?', 'What are some examples of cities that have successfully used gamification or participatory budgeting to gain public support for sustainable transportation initiatives?']\n",
            "\n",
            "🔍 RiskAnalyst_Epsilon critiquing DataScientist_Alpha's analysis...\n",
            "💬 RiskAnalyst_Epsilon → DataScientist_Alpha:\n",
            "   Critique: The data science perspective is valuable for quantifying and modeling the transportation system. However, from a risk ma...\n",
            "   Questions: ['What measures will be taken to ensure the accuracy and reliability of the data used for modeling?', 'How will potential risks, such as cost overruns and delays, be factored into the economic viability assessment?', 'What strategies will be implemented to ensure compliance with environmental regulations and safety standards?', 'How will public acceptance and adoption of new transportation strategies be promoted and potential resistance mitigated?']\n",
            "\n",
            "🔬 PHASE 3: Solution Synthesis\n",
            "\n",
            "🎯 DataScientist_Alpha synthesizing final solution...\n",
            "🏆 DataScientist_Alpha Final Solution:\n",
            "   ```json\n",
            "{\n",
            "    \"agent_id\": \"DataScientist_Alpha\",\n",
            "    \"main_response\": \"Okay, I've reviewed the analyses from the other agents. It seems we have a good foundation covering data analysis needs, user exp...\n",
            "   Confidence: 0.74\n",
            "   Next Action: Continue analysis\n",
            "\n",
            "🎯 ProductManager_Beta synthesizing final solution...\n",
            "🏆 ProductManager_Beta Final Solution:\n",
            "   ```json\n",
            "{\n",
            "    \"agent_id\": \"ProductManager_Beta\",\n",
            "    \"main_response\": \"Okay team, I've reviewed everyone's analyses. It's clear we have a solid foundation, but we need to synthesize these viewpoints i...\n",
            "   Confidence: 0.62\n",
            "   Next Action: Continue analysis\n",
            "\n",
            "🎯 TechArchitect_Gamma synthesizing final solution...\n",
            "🏆 TechArchitect_Gamma Final Solution:\n",
            "   ```json\n",
            "{\n",
            "    \"agent_id\": \"TechArchitect_Gamma\",\n",
            "    \"main_response\": \"Okay team, I've reviewed everyone's analyses and see some strong common threads. We all agree on the need for a multi-faceted app...\n",
            "   Confidence: 0.69\n",
            "   Next Action: Continue analysis\n",
            "\n",
            "🎯 CreativeInnovator_Delta synthesizing final solution...\n",
            "🏆 CreativeInnovator_Delta Final Solution:\n",
            "   ```json\n",
            "{\n",
            "    \"agent_id\": \"CreativeInnovator_Delta\",\n",
            "    \"main_response\": \"Okay team, let's ignite some real change! Agent 4, I love your audacity! We need to leapfrog, not just incrementally improve....\n",
            "   Confidence: 0.76\n",
            "   Next Action: Continue analysis\n",
            "\n",
            "🎯 RiskAnalyst_Epsilon synthesizing final solution...\n",
            "🏆 RiskAnalyst_Epsilon Final Solution:\n",
            "   ```json\n",
            "{\n",
            "    \"agent_id\": \"RiskAnalyst_Epsilon\",\n",
            "    \"main_response\": \"After reviewing the analyses from the team, several key themes and potential risks emerge. Agent 1 (Data Science) highlights the ...\n",
            "   Confidence: 0.75\n",
            "   Next Action: Continue analysis\n",
            "\n",
            "🤝 PHASE 4: Consensus & Recommendation\n",
            "📊 Average Solution Confidence: 0.71\n",
            "\n",
            "🏅 Most Confident Solution from: CreativeInnovator_Delta\n",
            "📝 Recommended Solution: ```json\n",
            "{\n",
            "    \"agent_id\": \"CreativeInnovator_Delta\",\n",
            "    \"main_response\": \"Okay team, let's ignite some real change! Agent 4, I love your audacity! We need to leapfrog, not just incrementally improve....\n",
            "\n",
            "💡 Collective Intelligence Insights:\n",
            "   1. Insight from Technical Architecture & Engineering Lead\n",
            "   2. Insight from Product Strategy & User Experience Expert\n",
            "   3. Insight from Innovation & Creative Problem Solving Specialist\n",
            "   4. Insight from Risk Management & Compliance Expert\n",
            "   5. Insight from Data Scientist & Analytics Specialist\n",
            "\n",
            "===========================⏸️  BREAK BETWEEN SESSIONS===========================\n",
            "\n",
            "===========================🌟 COLLABORATION SESSION 2============================\n",
            "\n",
            "🎯 Multi-Gemini Collaborative Problem Solving\n",
            "🔍 Problem: Create a strategy for a tech startup to compete against established players in the AI-powered healthcare diagnostics market.\n",
            "================================================================================\n",
            "\n",
            "📊 PHASE 1: Individual Agent Analysis\n",
            "\n",
            "🧠 DataScientist_Alpha analyzing...\n",
            "✅ DataScientist_Alpha (Data Scientist & Analytics Specialist):\n",
            "   Response: ```json\n",
            "{\n",
            "    \"agent_id\": \"DataScientist_Alpha\",\n",
            "    \"main_response\": \"Okay, from a data science perspective, competing in the AI-powered healthcare d...\n",
            "   Confidence: 0.88\n",
            "   Key Insights: ['Insight from Data Scientist & Analytics Specialist']\n",
            "\n",
            "🧠 ProductManager_Beta analyzing...\n",
            "✅ ProductManager_Beta (Product Strategy & User Experience Expert):\n",
            "   Response: From a product and UX perspective, competing in the AI-powered healthcare diagnostics market against established players requires a laser focus on use...\n",
            "   Confidence: 0.90\n",
            "   Key Insights: ['Focus on specific user segments with unmet needs to differentiate from established players.', 'Prioritize user trust through transparency and explainability of AI-driven diagnostics.', 'Ensure seamless integration with existing healthcare workflows to minimize disruption and maximize adoption.']\n",
            "\n",
            "🧠 TechArchitect_Gamma analyzing...\n",
            "✅ TechArchitect_Gamma (Technical Architecture & Engineering Lead):\n",
            "   Response: From a technical architecture and engineering perspective, competing in the AI-powered healthcare diagnostics market requires a strategy built on a ro...\n",
            "   Confidence: 0.90\n",
            "   Key Insights: ['Scalability and cost-effectiveness are critical for competing with established players.', 'Explainability and auditability of AI models are essential for regulatory compliance and trust.', 'Seamless integration with existing healthcare systems is crucial for adoption.']\n",
            "\n",
            "🧠 CreativeInnovator_Delta analyzing...\n",
            "✅ CreativeInnovator_Delta (Innovation & Creative Problem Solving Specialist):\n",
            "   Response: This is exciting! Competing against established players in AI healthcare diagnostics requires a radical, not incremental, approach. We can't just be '...\n",
            "   Confidence: 0.80\n",
            "   Key Insights: ['Focusing on a niche market underserved by larger players allows for specialization and rapid innovation.', 'Leveraging unconventional data sources (e.g., wearable sensor data, environmental factors) for preventative diagnostics can create a unique selling proposition.', 'A hyper-personalized diagnostic approach, perhaps using federated learning to train models on individual patient data without compromising privacy, could be a game-changer.']\n",
            "\n",
            "🧠 RiskAnalyst_Epsilon analyzing...\n",
            "✅ RiskAnalyst_Epsilon (Risk Management & Compliance Expert):\n",
            "   Response: From a risk management and compliance perspective, entering the AI-powered healthcare diagnostics market presents significant challenges for a startup...\n",
            "   Confidence: 0.80\n",
            "   Key Insights: ['Data privacy and security are paramount and require proactive measures to comply with global regulations.', 'Algorithm bias can lead to inaccurate diagnoses and discriminatory outcomes, necessitating rigorous testing and validation.', 'Regulatory approval pathways are complex and time-consuming, requiring early engagement with relevant agencies.', 'Intellectual property protection is crucial to maintain a competitive advantage and prevent infringement claims.']\n",
            "\n",
            "🔄 PHASE 2: Cross-Agent Critique & Feedback\n",
            "\n",
            "🔍 DataScientist_Alpha critiquing ProductManager_Beta's analysis...\n",
            "💬 DataScientist_Alpha → ProductManager_Beta:\n",
            "   Critique: The product and UX perspective is valuable, particularly the focus on user needs and trust. However, we need to quantify...\n",
            "   Questions: ['What specific data sources can we leverage to quantify the unmet needs and pain points of different user segments (e.g., claims data, EHR data, user surveys)?', 'How can we proactively identify and mitigate potential biases in our AI models across different demographic groups?']\n",
            "\n",
            "🔍 ProductManager_Beta critiquing TechArchitect_Gamma's analysis...\n",
            "💬 ProductManager_Beta → TechArchitect_Gamma:\n",
            "   Critique: I appreciate the thorough technical analysis. The emphasis on scalability, explainability, and integration is spot-on. H...\n",
            "   Questions: ['What specific user pain points are we targeting with our AI diagnostics solution?', 'How can we measure and track user satisfaction with our platform?', 'What are the key workflows we need to integrate with to create a seamless user experience?']\n",
            "\n",
            "🔍 TechArchitect_Gamma critiquing CreativeInnovator_Delta's analysis...\n",
            "💬 TechArchitect_Gamma → CreativeInnovator_Delta:\n",
            "   Critique: I agree with the overall sentiment of focusing on a niche and leveraging unconventional data. However, we need to ground...\n",
            "   Questions: ['What specific unconventional data sources are we considering, and what is the plan for validating their diagnostic value?', 'What is the estimated computational cost of the proposed hyper-personalized diagnostic approach, and how will we ensure scalability?', 'What are the regulatory considerations for using federated learning with sensitive patient data, and how will we address privacy concerns?']\n",
            "\n",
            "🔍 CreativeInnovator_Delta critiquing RiskAnalyst_Epsilon's analysis...\n",
            "💬 CreativeInnovator_Delta → RiskAnalyst_Epsilon:\n",
            "   Critique: While I appreciate the thorough risk assessment, we can't let regulatory hurdles completely stifle innovation. Focusing ...\n",
            "   Questions: ['What untapped data sources or diagnostic modalities are the established players overlooking?', 'How can we build a modular and adaptable AI diagnostic platform that can quickly pivot to address emerging regulatory changes or technological advancements?', 'Can we partner with patient advocacy groups or smaller clinics to build trust and gather real-world data in a more agile and cost-effective manner?']\n",
            "\n",
            "🔍 RiskAnalyst_Epsilon critiquing DataScientist_Alpha's analysis...\n",
            "💬 RiskAnalyst_Epsilon → DataScientist_Alpha:\n",
            "   Critique: While I appreciate the data-driven approach outlined by DataScientist_Alpha, I believe we need to explicitly address the...\n",
            "   Questions: ['LegalCounsel_Beta, can you provide a detailed overview of the regulatory landscape and compliance requirements for AI-powered diagnostics in our target market?', 'EthicalReviewBoard_Gamma, what are the key ethical considerations and best practices for developing and deploying AI diagnostic tools to ensure fairness and avoid bias?', 'DataScientist_Alpha, how can we proactively identify and mitigate potential biases in our training data and algorithms?']\n",
            "\n",
            "🔬 PHASE 3: Solution Synthesis\n",
            "\n",
            "🎯 DataScientist_Alpha synthesizing final solution...\n",
            "🏆 DataScientist_Alpha Final Solution:\n",
            "   ```json\n",
            "{\n",
            "    \"agent_id\": \"DataScientist_Alpha\",\n",
            "    \"main_response\": \"Synthesizing the perspectives, a clear strategy emerges for the startup to compete effectively. The common themes revolve around ...\n",
            "   Confidence: 0.72\n",
            "   Next Action: Continue analysis\n",
            "\n",
            "🎯 ProductManager_Beta synthesizing final solution...\n",
            "🏆 ProductManager_Beta Final Solution:\n",
            "   ```json\n",
            "{\n",
            "    \"agent_id\": \"ProductManager_Beta\",\n",
            "    \"main_response\": \"Okay, synthesizing the various perspectives, a clear strategy emerges that balances innovation with user trust and regulatory com...\n",
            "   Confidence: 0.68\n",
            "   Next Action: Continue analysis\n",
            "\n",
            "🎯 TechArchitect_Gamma synthesizing final solution...\n",
            "🏆 TechArchitect_Gamma Final Solution:\n",
            "   ```json\n",
            "{\n",
            "    \"agent_id\": \"TechArchitect_Gamma\",\n",
            "    \"main_response\": \"Okay, synthesizing the various perspectives, a viable strategy for a tech startup to compete in the AI-powered healthcare diagnos...\n",
            "   Confidence: 0.88\n",
            "   Next Action: Continue analysis\n",
            "\n",
            "🎯 CreativeInnovator_Delta synthesizing final solution...\n",
            "🏆 CreativeInnovator_Delta Final Solution:\n",
            "   ```json\n",
            "{\n",
            "    \"agent_id\": \"CreativeInnovator_Delta\",\n",
            "    \"main_response\": \"Alright, let's ditch the incremental thinking and aim for a paradigm shift! Everyone's pointing out the giants' strengths (da...\n",
            "   Confidence: 0.81\n",
            "   Next Action: Continue analysis\n",
            "\n",
            "🎯 RiskAnalyst_Epsilon synthesizing final solution...\n",
            "🏆 RiskAnalyst_Epsilon Final Solution:\n",
            "   ```json\n",
            "{\n",
            "    \"agent_id\": \"RiskAnalyst_Epsilon\",\n",
            "    \"main_response\": \"After reviewing the analyses from the other agents, several key themes emerge. DataScientist_Alpha highlights the importance of d...\n",
            "   Confidence: 0.66\n",
            "   Next Action: Continue analysis\n",
            "\n",
            "🤝 PHASE 4: Consensus & Recommendation\n",
            "📊 Average Solution Confidence: 0.75\n",
            "\n",
            "🏅 Most Confident Solution from: TechArchitect_Gamma\n",
            "📝 Recommended Solution: ```json\n",
            "{\n",
            "    \"agent_id\": \"TechArchitect_Gamma\",\n",
            "    \"main_response\": \"Okay, synthesizing the various perspectives, a viable strategy for a tech startup to compete in the AI-powered healthcare diagnos...\n",
            "\n",
            "💡 Collective Intelligence Insights:\n",
            "   1. Insight from Technical Architecture & Engineering Lead\n",
            "   2. Insight from Product Strategy & User Experience Expert\n",
            "   3. Insight from Innovation & Creative Problem Solving Specialist\n",
            "   4. Insight from Risk Management & Compliance Expert\n",
            "   5. Insight from Data Scientist & Analytics Specialist\n",
            "\n",
            "🎉 Multi-Gemini Agent2Agent Collaboration Complete!\n",
            "💡 This demonstrates true AI-to-AI collaboration using Google's Gemini models!\n",
            "🤖 Each agent brought unique expertise to solve complex problems collectively!\n"
          ]
        }
      ]
    }
  ]
}